Displaying similar documents to “Two families of normality tests based on Polya characterization, and their efficiency.”

Normalization of the Kolmogorov–Smirnov and Shapiro–Wilk tests of normality

Zofia Hanusz, Joanna Tarasińska (2015)

Biometrical Letters

Similarity:

Two very well-known tests for normality, the Kolmogorov-Smirnov and the Shapiro- Wilk tests, are considered. Both of them may be normalized using Johnson’s (1949) SB distribution. In this paper, functions for normalizing constants, dependent on the sample size, are given. These functions eliminate the need to use non-standard statistical tables with normalizing constants, and make it easy to obtain p-values for testing normality.

The behavior of locally most powerful tests

Marek Omelka (2005)

Kybernetika

Similarity:

The locally most powerful (LMP) tests of the hypothesis H : θ = θ 0 against one-sided as well as two-sided alternatives are compared with several competitive tests, as the likelihood ratio tests, the Wald-type tests and the Rao score tests, for several distribution shapes and for location, shape and vector parameters. A simulation study confirms the importance of the condition of local unbiasedness of the test, and shows that the LMP test can sometimes dominate the other tests only in a very restricted...

Power comparison of Rao′s score test, the Wald test and the likelihood ratio test in (2xc) contingency tables

Anita Dobek, Krzysztof Moliński, Ewa Skotarczak (2015)

Biometrical Letters

Similarity:

There are several statistics for testing hypotheses concerning the independence of the distributions represented by two rows in contingency tables. The most famous are Rao′s score, the Wald and the likelihood ratio tests. A comparison of the power of these tests indicates the Wald test as the most powerful.

On the role played by the fixed bandwidth in the Bickel-Rosenblatt goodness-of-fit test.

Carlos Tenreiro (2005)

SORT

Similarity:

For the Bickel-Rosenblatt goodness-of-fit test with fixed bandwidth studied by Fan (1998) we derive its Bahadur exact slopes in a neighbourhood of a simple hypothesis f = f and we use them to get a better understanding on the role played by the smoothing parameter in the detection of departures from the null hypothesis. When f is an univariate normal distribution and we take for kernel the standard normal density function, we compute these slopes for a set of Edgeworth alternatives which...